Suicide risk assessment has traditionally focused on predicting future behavior by categorizing individuals as "low," "moderate," or "high" risk of suicide. However, evidence shows that these classifications lack reliability and practical utility. A prevention-oriented suicide risk assessment approach shifts the focus from assigning static labels to evincing addressable factors that guide clinicians toward actionable, tailored strategies that mitigate risk.
This approach has been implemented in various healthcare settings, demonstrating its potential to reduce suicidal behavior by emphasizing engagement, contextual factors, and resource availability.
This Research Topic aims to improve suicide prevention efforts by exploring risk assessment models that go beyond mere prediction. The central question is: What risk formulation models are better at preventing suicide?
We seek to highlight approaches that replace traditional predictive models with practical frameworks that inform intervention. This collection will bring together researchers and clinicians to examine how structured, prevention-focused assessments can lead to better clinical outcomes. Contributions may include evaluations of existing models, new methodologies and AI-based approaches, and real-world applications of prevention-focused risk assessment.
We welcome submissions that examine the theoretical foundations, empirical research, and clinical applications of prevention-oriented suicide risk assessment. Contributions from psychiatry, psychology, public health, and related disciplines are encouraged. Topics may include:
1. Conceptual Advances: Evolving theories on prevention-oriented risk formulation and how they contrast with traditional models.
2. Empirical Research: Studies on the effectiveness of prevention-focused risk assessments in reducing suicide attempts and improving care.
3. Clinical Practice: Implementation strategies across different healthcare settings, from inpatient care to community-based interventions.
4. Training and Education: Methods for teaching prevention-oriented risk assessment to mental health professionals.
5. Technology and Innovation: The role of digital tools, artificial intelligence, and data analytics in refining risk assessments.
6. Ethical and Policy Issues: Considerations related to adopting prevention-oriented risk assessment in clinical and policy frameworks.
We invite original research, systematic reviews, hypothesis and theory, and perspectives that contribute to advancing prevention-focused suicide risk assessment. In general, authors contributing quantitative data are strongly encouraged to submit their works. The goal is to develop more effective, practical, and person-centered strategies for suicide prevention.
Conflict of Interest: Anthony R. Pisani is an equity owner of SafeSide Prevention, which receives fees for consultation and education. The University of Rochester receives royalties from SafeSide Prevention and declares this interest
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Clinical Trial
Community Case Study
Conceptual Analysis
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
FAIR² Data
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Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
